Commit message: Prevent heap OOB read in TFLite's `gather_nd.cc`.

Passing negative indices is illegal but there was a missing check so that resulted in OOB accesses.

PiperOrigin-RevId: 387208551
Change-Id: I6b7a8a62d3e7c13a16d81619e5bc23ae2cdbc7fd
CVE: CVE-2021-37687
CWE: CWE-125
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`. Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Project: tensorflow
File: gather_nd.cc

TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);

  const TfLiteTensor* params;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kParams, &params));
  const TfLiteTensor* indices;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kIndices, &indices));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context,
                    GetOutputSafe(context, node, kOutputTensor, &output));

  switch (params->type) {
    case kTfLiteFloat32:
    case kTfLiteUInt8:
    case kTfLiteInt8:
    case kTfLiteInt16:
    case kTfLiteInt64:
    case kTfLiteInt32:
    case kTfLiteString:
      break;
    default:
      context->ReportError(
          context, "Params of type '%s' are not supported by gather_nd.",
          TfLiteTypeGetName(params->type));
      return kTfLiteError;
  }
  switch (indices->type) {
    case kTfLiteInt64:
    case kTfLiteInt32:
      break;
    default:
      context->ReportError(
          context, "Indices of type '%s' are not supported by gather_nd.",
          TfLiteTypeGetName(indices->type));
      return kTfLiteError;
  }

  const int params_rank = NumDimensions(params);
  const int indices_rank = NumDimensions(indices);
  const int indices_nd = SizeOfDimension(indices, indices_rank - 1);
  if (params_rank < 1) {
    context->ReportError(context, "Params must be at least a vector.");
    return kTfLiteError;
  }
  if (indices_rank < 1) {
    context->ReportError(context, "Indices must be at least a vector.");
    return kTfLiteError;
  }
  if (indices_nd > params_rank) {
    context->ReportError(
        context, "Index innermost dimension length must be <= params rank.");
    return kTfLiteError;
  }

  // Assign to output the input type.
  output->type = params->type;

  // The result shape is
  // indices.shape[:-1] + params.shape[indices.shape[-1]:]
  const int output_rank = indices_rank + params_rank - indices_nd - 1;
  TfLiteIntArray* output_shape = TfLiteIntArrayCreate(output_rank);
  int output_index = 0;
  for (int i = 0; i < indices_rank - 1; ++i) {
    output_shape->data[output_index++] = indices->dims->data[i];
  }
  for (int i = indices_nd; i < params_rank; ++i) {
    output_shape->data[output_index++] = params->dims->data[i];
  }
  return context->ResizeTensor(context, output, output_shape);
}

Project: tensorflow
File: gather_nd.cc

TfLiteStatus EvalGatherNd(TfLiteContext* context, const TfLiteTensor* params,
                          const TfLiteTensor* indices, TfLiteTensor* output) {
  bool indices_has_only_positive_elements = true;
  const auto* indices_values = GetTensorData<IndicesT>(indices);
  const size_t num_indices = indices->bytes / sizeof(IndicesT);
  for (size_t i = 0; i < num_indices; i++) {
    if (indices_values[i] < 0) {
      indices_has_only_positive_elements = false;
      break;
    }
  }
  TF_LITE_ENSURE(context, indices_has_only_positive_elements);

  switch (params->type) {
    case kTfLiteFloat32:
      return GatherNd<float, IndicesT>(params, indices, output);
    case kTfLiteUInt8:
      return GatherNd<uint8_t, IndicesT>(params, indices, output);
    case kTfLiteInt8:
      return GatherNd<int8_t, IndicesT>(params, indices, output);
    case kTfLiteInt16:
      return GatherNd<int16_t, IndicesT>(params, indices, output);
    case kTfLiteInt32:
      return GatherNd<int32_t, IndicesT>(params, indices, output);
    case kTfLiteInt64:
      return GatherNd<int64_t, IndicesT>(params, indices, output);
    case kTfLiteString:
      return GatherNdString<IndicesT>(params, indices, output);
    default:
      context->ReportError(context,
                           "Params type '%s' are not supported by gather_nd.",
                           TfLiteTypeGetName(params->type));
      return kTfLiteError;
  }
}

Project: tensorflow
File: gather_nd.cc

TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteTensor* params;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kParams, &params));
  const TfLiteTensor* indices;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kIndices, &indices));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context,
                    GetOutputSafe(context, node, kOutputTensor, &output));

  // Prevent division by 0 in the helper
  TF_LITE_ENSURE(context, NumElements(params) > 0);

  switch (indices->type) {
    case kTfLiteInt32:
      return EvalGatherNd<int32_t>(context, params, indices, output);
    case kTfLiteInt64:
      return EvalGatherNd<int64_t>(context, params, indices, output);
    default:
      context->ReportError(
          context, "Indices of type '%s' are not supported by gather_nd.",
          TfLiteTypeGetName(indices->type));
      return kTfLiteError;
  }
}